---
title: "The Prescription Takeover"
subtitle: "U.S. Google searches show a dramatic shift: combined interest in GLP-1 drugs (Ozempic, Wegovy, Mounjaro) has overtaken fad diets (keto, Paleo) since late 2023."
description: "placeholder"
date: "2026-01-08"
author:
- name: "Steven Ponce"
url: "https://stevenponce.netlify.app"
citation:
url: "https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2026/tt_2026_01.html"
categories: ["TidyTuesday", "Data Visualization", "R Programming", "2026"]
tags: [
"Lplaceholder",
]
image: "thumbnails/tt_2026_01.png"
format:
html:
toc: true
toc-depth: 5
code-link: true
code-fold: true
code-tools: true
code-summary: "Show code"
self-contained: true
theme:
light: [flatly, assets/styling/custom_styles.scss]
dark: [darkly, assets/styling/custom_styles_dark.scss]
editor_options:
chunk_output_type: inline
execute:
freeze: true
cache: true
error: false
message: false
warning: false
eval: true
---
 {#fig-1}
### <mark> **Steps to Create this Graphic** </mark>
#### 1. Load Packages & Setup
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages ({
if (! require ("pacman" )) install.packages ("pacman" )
pacman:: p_load (
tidyverse, # Easily Install and Load the 'Tidyverse'
ggtext, # Improved Text Rendering Support for 'ggplot2'
showtext, # Using Fonts More Easily in R Graphs
ggrepel, # Non-overlapping Text Labels
janitor, # Simple Tools for Examining and Cleaning Dirty Data
scales, # Scale Functions for Visualization
glue # Interpreted String Literals
)
})
### |- figure size ----
camcorder:: gg_record (
dir = here:: here ("temp_plots" ),
device = "png" ,
width = 8 ,
height = 6 ,
units = "in" ,
dpi = 320
)
# Source utility functions
suppressMessages (source (here:: here ("R/utils/fonts.R" )))
source (here:: here ("R/utils/social_icons.R" ))
source (here:: here ("R/utils/image_utils.R" ))
source (here:: here ("R/themes/base_theme.R" ))
```
#### 2. Read in the Data
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
# pak::pkg_install('gtrendsR')
# library(gtrendsR)
# library(tidyverse)
# Pull first batch
# terms_diet <- c("diet", "keto", "intermittent fasting", "calorie counting", "Ozempic")
# trends_diet <- gtrends(
# keyword = terms_diet,
# geo = "US",
# time = "2016-01-01 2025-01-07"
# )
# Pull second batch
# terms_expanded <- c("Wegovy", "Mounjaro", "Paleo")
# trends_expanded <- gtrends(
# keyword = terms_expanded,
# geo = "US",
# time = "2016-01-01 2025-01-07"
# )
# Combine and clean
# diet_combined <- bind_rows(
# trends_diet$interest_over_time,
# trends_expanded$interest_over_time
# ) |>
# mutate(
# hits = case_when(
# hits == "<1" ~ "0.5",
# TRUE ~ hits
# ),
# hits = as.numeric(hits)
# )
# Save combined data
# write_csv(diet_combined, "2026/Week_01/diet_trends_combined.csv")
raw_data <- read_csv (
here:: here ("data/TidyTuesday/2026/diet_trends_combined.csv" )) |>
clean_names ()
```
#### 3. Examine the Data
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false
glimpse (raw_data)
```
#### 4. Tidy Data
```{r}
#| label: tidy-fixed
#| warning: false
### |- clean individual terms ----
diet_clean <- raw_data |>
mutate (
hits = case_when (
hits == "<1" ~ 0.5 ,
TRUE ~ as.numeric (hits)
),
date = as.Date (date),
year = year (date),
month = month (date)
) |>
select (date, year, month, keyword, hits)
### |- create grouped categories ----
diet_grouped <- diet_clean |>
mutate (
category = case_when (
keyword %in% c ("keto" , "Paleo" ) ~ "Fad Diets" ,
keyword %in% c ("Ozempic" , "Wegovy" , "Mounjaro" ) ~ "GLP-1 Drugs" ,
keyword == "diet" ~ "diet" ,
TRUE ~ "Other"
)
) |>
# Exclude minor terms
filter (category != "Other" ) |>
group_by (date, year, month, category) |>
summarise (
hits = sum (hits, na.rm = TRUE ),
.groups = "drop"
)
### |- find the crossover point ----
crossover_data <- diet_grouped |>
filter (category %in% c ("Fad Diets" , "GLP-1 Drugs" )) |>
pivot_wider (names_from = category, values_from = hits) |>
filter (` GLP-1 Drugs ` > ` Fad Diets ` ) |>
slice_min (date, n = 1 )
crossover_date <- crossover_data$ date
crossover_date
# Get the intersection y-value for annotation
crossover_y <- crossover_data$ ` GLP-1 Drugs `
# Create label data for end of lines
label_data <- diet_grouped |>
filter (date == max (date)) |>
mutate (
label = case_when (
category == "GLP-1 Drugs" ~ "GLP-1 Drugs \n (Ozempic, Wegovy, Mounjaro)" ,
category == "Fad Diets" ~ "Fad Diets \n (keto, Paleo)" ,
TRUE ~ category
)
)
# Define prescription era start
prescription_era_start <- as.Date ("2023-10-01" )
```
#### 5. Visualization Parameters
```{r}
#| label: params
#| include: true
#| warning: false
### |- plot aesthetics ----
colors <- get_theme_colors (
palette = list (
"diet" = "gray50" ,
"Fad Diets" = "#3498db" ,
"GLP-1 Drugs" = "#e74c3c"
)
)
### |- titles and caption ----
title_text <- str_glue ("The Prescription Takeover" )
subtitle_text <- str_glue (
"U.S. Google searches show a dramatic shift: combined interest in <span style='color:#e74c3c;'>**GLP-1 drugs**</span><br>" ,
"(Ozempic, Wegovy, Mounjaro) has overtaken <span style='color:#3498db;'>**fad diets**</span> (keto, Paleo) since late 2023."
)
caption_text <- create_social_caption (
tt_year = 2026 ,
tt_week = 01 ,
source_text = "Google Trends (US), 2016-2025 via { gtrendsR }"
)
### |- fonts ----
setup_fonts ()
fonts <- get_font_families ()
### |- plot theme ----
# Start with base theme
base_theme <- create_base_theme (colors)
# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme (
base_theme,
theme (
# Text styling
plot.title = element_text (
face = "bold" , family = fonts$ title, size = rel (1.4 ),
color = colors$ title, margin = margin (b = 10 ), hjust = 0
),
plot.subtitle = element_markdown (
face = "italic" , family = fonts$ subtitle, lineheight = 1.2 ,
color = colors$ subtitle, size = rel (0.9 ), margin = margin (b = 20 ), hjust = 0
),
# Grid
panel.grid.minor = element_blank (),
panel.grid.major.x = element_blank (),
panel.grid.major = element_line (color = "gray90" , linewidth = 0.25 ),
# Axes
axis.title = element_text (size = rel (0.8 ), color = "gray30" ),
axis.text = element_text (color = "gray30" ),
axis.text.y = element_text (size = rel (0.85 )),
axis.ticks = element_blank (),
# Facets
strip.background = element_rect (fill = "gray95" , color = NA ),
strip.text = element_text (
face = "bold" ,
color = "gray20" ,
size = rel (0.9 ),
margin = margin (t = 6 , b = 4 )
),
panel.spacing = unit (1.5 , "lines" ),
# Legend elements
legend.position = "plot" ,
legend.title = element_text (
family = fonts$ subtitle,
color = colors$ text, size = rel (0.8 ), face = "bold"
),
legend.text = element_text (
family = fonts$ tsubtitle,
color = colors$ text, size = rel (0.7 )
),
legend.margin = margin (t = 15 ),
# Plot margin
plot.margin = margin (20 , 20 , 20 , 20 ),
)
)
# Set theme
theme_set (weekly_theme)
```
#### 6. Plot
```{r}
#| label: plot
#| warning: false
### |- Plot ----
p <- diet_grouped |>
ggplot (aes (x = date, y = hits, color = category)) +
# Geoms
geom_line (linewidth = 0.4 , alpha = 0.25 ) +
geom_smooth (
method = "loess" ,
span = 0.2 ,
se = FALSE ,
linewidth = 1.8
) +
geom_text_repel (
data = label_data |>
filter (category == "GLP-1 Drugs" ),
aes (label = label),
hjust = 0 ,
direction = "y" ,
nudge_x = 50 ,
nudge_y = 5 ,
segment.color = NA ,
family = fonts$ text,
fontface = "bold" ,
size = 3 ,
lineheight = 0.85 ,
box.padding = 0.5
) +
geom_text_repel (
data = label_data |>
filter (category != "GLP-1 Drugs" ),
aes (label = label),
hjust = 0 ,
direction = "y" ,
nudge_x = 50 ,
# nudge_y = 5,
segment.color = NA ,
family = fonts$ text,
fontface = "bold" ,
size = 3 ,
lineheight = 0.85 ,
box.padding = 0.5
) +
# Annotate
annotate (
"rect" ,
xmin = prescription_era_start,
xmax = max (diet_grouped$ date) + 60 ,
ymin = - Inf ,
ymax = Inf ,
fill = "#e74c3c" ,
alpha = 0.05
) +
annotate (
"text" ,
x = prescription_era_start + 200 ,
y = 130 ,
label = "Prescription Era" ,
family = fonts$ text,
size = 3 ,
color = "gray30" ,
fontface = "bold.italic"
) +
annotate (
"point" ,
x = crossover_date,
y = crossover_y,
size = 3 ,
color = "gray20"
) +
annotate (
"text" ,
x = crossover_date - 90 ,
y = crossover_y + 15 ,
label = "GLP-1 surpasses \n fad diets" ,
family = fonts$ text,
size = 2.8 ,
color = "gray30" ,
hjust = 1 ,
lineheight = 0.9
) +
annotate (
"curve" ,
x = crossover_date - 80 ,
y = crossover_y + 10 ,
xend = crossover_date - 10 ,
yend = crossover_y + 2 ,
curvature = 0.2 ,
arrow = arrow (length = unit (0.15 , "cm" ), type = "closed" ),
color = "gray40" ,
linewidth = 0.4
) +
# Scales
scale_color_manual (
values = c (
"diet" = "gray50" ,
"Fad Diets" = "#3498db" ,
"GLP-1 Drugs" = "#e74c3c"
)
) +
scale_x_date (
date_breaks = "2 years" ,
date_labels = "%Y" ,
expand = expansion (mult = c (0.02 , 0.18 ))
) +
scale_y_continuous (
breaks = seq (0 , 100 , 25 ),
limits = c (0 , 130 ),
expand = expansion (mult = c (0 , 0.02 ))
) +
coord_cartesian (clip = "off" ) +
# Labs
labs (
title = title_text,
subtitle = subtitle_text,
caption = caption_text,
y = "Combined Search Interest"
) +
# Theme
theme (
plot.title = element_markdown (
size = rel (2.3 ),
family = fonts$ title,
face = "bold" ,
color = colors$ title,
lineheight = 1.15 ,
margin = margin (t = 8 , b = 5 )
),
plot.subtitle = element_markdown (
size = rel (0.9 ),
family = fonts$ subtitle,
color = alpha (colors$ subtitle, 0.88 ),
lineheight = 1.5 ,
margin = margin (t = 5 , b = 20 )
),
plot.caption = element_markdown (
size = rel (0.65 ),
family = fonts$ subtitle,
color = colors$ caption,
hjust = 0 ,
lineheight = 1.4 ,
margin = margin (t = 20 , b = 5 )
)
)
```
#### 7. Save
```{r}
#| label: save
#| warning: false
### |- plot image ----
save_plot (
plot = p,
type = "tidytuesday" ,
year = 2026 ,
week = 01 ,
width = 8 ,
height = 6 ,
)
```
#### 8. Session Info
::: {.callout-tip collapse="true"}
##### Expand for Session Info
```{r, echo = FALSE}
#| eval: true
#| warning: false
sessionInfo ()
```
:::
#### 9. GitHub Repository
::: {.callout-tip collapse="true"}
##### Expand for GitHub Repo
The complete code for this analysis is available in [ `tt_2026_01.qmd` ](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2026_01.qmd) .
For the full repository, [ click here ](https://github.com/poncest/personal-website/) .
:::
#### 10. References
::: {.callout-tip collapse="true"}
##### Expand for References
1. **Data Source:**
- TidyTuesday 2026 Week 01: [ Bring your own data ](https://github.com/rfordatascience/tidytuesday/blob/main/data/2026/2026-01-06/readme.md)
- [ Google Trends ](https://trends.google.com/) - Search interest data for U.S., 2016-2025
- Search terms: "diet", "keto", "Paleo", "Ozempic", "Wegovy", "Mounjaro"
2. **R Package:**
- Massicotte, P. and Eddelbuettel, D. (2024). gtrendsR: Perform and Display Google Trends Queries. R package. [ CRAN ](https://cran.r-project.org/package=gtrendsR) | [ GitHub ](https://github.com/PMassicotte/gtrendsR)
:::
#### 11. Custom Functions Documentation
::: {.callout-note collapse="true"}
##### 📦 Custom Helper Functions
This analysis uses custom functions from my personal module library for efficiency and consistency across projects.
**Functions Used:**
- **`fonts.R`**: `setup_fonts()` , `get_font_families()` - Font management with showtext
- **`social_icons.R`**: `create_social_caption()` - Generates formatted social media captions
- **`image_utils.R`**: `save_plot()` - Consistent plot saving with naming conventions
- **`base_theme.R`**: `create_base_theme()` , `extend_weekly_theme()` , `get_theme_colors()` - Custom ggplot2 themes
**Why custom functions?**\
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.
**Source Code:**\
View all custom functions → [ GitHub: R/utils ](https://github.com/poncest/personal-website/tree/master/R)
:::